A proposal for further strengthening science in environmental impact assessment in Canada
Bibliographic record
Abstract
We observe ongoing weaknesses in the quality of science underpinning environmental impact assessment (EIA) in Canada. This is frustrating because approaches for strong scientific practice in EIA were published decades ago. A major failing has been the lack of scientific support from outside the EIA practitioner community. We argue for a re-conception of science associated with EIA that includes a rigorous scholarship of application inside EIA and a vigorous scholarship of integration outside it. Cases of exemplary organizational structures and science applications in the Canadian forest sector are given. To turn EIA from the often bitter battleground of shallow impact debates to an enterprise of strong accumulation of effects knowledge, we urge the relevant communities of researchers and practitioners to become embedded communities of practice and reform the way science contributes to EIA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.025 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".